mcpcute

MCP aggregator - aggregate multiple MCPs behind a single interface to reduce context pollution for AI agents.

Instead of exposing 20+ MCP tools directly to your AI agent, mcpcute provides a two-level hierarchy with just 7 tools:

MCP-Level Operations

1. list_mcps - List all available MCP servers with their connection status
2. search_mcps - Search for MCP servers by name
3. get_mcp_details - Get detailed info about an MCP including its tools

Tool-Level Operations

4. list_tools - List all tools for a specific MCP
5. search_tools - Search for tools (optionally scoped to a specific MCP)
6. get_tool_details - Get detailed schema and description for a tool
7. execute_tool - Execute a tool with the given arguments

Installation

npm install -g mcpcute
# or
bun add -g mcpcute
# or
npx mcpcute

Configuration

Create a mcpcute.config.json file in your working directory (or set MCPCUTE_CONFIG env var to point to your config file):

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
    },
    "fetch": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-fetch"]
    }
  }
}

Usage

With Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcpcute": {
      "command": "npx",
      "args": ["-y", "mcpcute"],
      "env": {
        "MCPCUTE_CONFIG": "/path/to/your/mcpcute.config.json"
      }
    }
  }
}

With Claude Code

Add to your Claude Code MCP settings:

{
  "mcpServers": {
    "mcpcute": {
      "command": "npx",
      "args": ["-y", "mcpcute"],
      "env": {
        "MCPCUTE_CONFIG": "/path/to/your/mcpcute.config.json"
      }
    }
  }
}

Standalone

# With global install
mcpcute

# Or with npx
npx mcpcute

# With custom config path
MCPCUTE_CONFIG=/path/to/config.json npx mcpcute

How it works

1. mcpcute starts instantly - no upfront connections to any MCP servers
2. Use list_mcps or search_mcps to discover available MCPs (no connections needed)
3. Use get_mcp_details or list_tools to explore an MCP's capabilities (connects on-demand)
4. Use search_tools to find tools across all MCPs or scoped to one
5. Use get_tool_details to get the full schema for a tool
6. Use execute_tool to run the tool

This reduces the initial context from potentially hundreds of tool schemas to just 7 simple tools, and startup is instant regardless of how many MCPs you configure.

Workflow Examples

Discovering Linear functionality

1. search_mcps("linear") → finds "kyle-linear" MCP
2. list_tools("kyle-linear") → shows all Linear tools
3. get_tool_details("linear_search_issues") → see how to use it
4. execute_tool("linear_search_issues", {...}) → run it

Exploring all available MCPs

1. list_mcps() → see all configured MCPs
2. get_mcp_details("yeego-pocketbase") → learn about this MCP
3. list_tools("yeego-pocketbase") → see what it can do

Why mcpcute?

• Instant startup: Lazy loading means no waiting for 20+ MCP servers to connect
• Two-level hierarchy: Clear separation between MCP discovery and tool discovery
• Reduced context pollution: Instead of loading 50+ tool schemas into your AI's context, load just 7
• Dynamic tool discovery: AI agents can search and discover tools as needed
• Scoped exploration: Explore one MCP at a time instead of being overwhelmed
• Unified interface: One consistent API for all your MCP tools
• Easy configuration: Simple JSON config to aggregate multiple MCP servers

License

MIT